Ships in days Fortune 500. Return real logistics status for an order id: reads the live order record if one exists, otherwise returns a clear status and routes to the concierge to link the order. No fabricated tracking.
| Name | Required | Description | Default |
|---|---|---|---|
| order_id | Yes | Order id |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses key behaviors: it reads the live order record if it exists, and does not fabricate tracking data ('No fabricated tracking'). It also explains the fallback action for missing orders. This is meaningful transparency, though it omits details like authentication or rate limits, so a 4 is appropriate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short but opens with the confusing phrase 'Ships in days Fortune 500', which is not explained and does not contribute to understanding the tool's purpose or usage. The rest is concise and clear, but the irrelevant opener prevents a higher score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description explains the main flow (returns real status, handles missing order) and explicitly states no fabrication. There is no output schema, so the description bears responsibility for return behavior, which it covers reasonably well. However, it does not specify the exact structure of the returned 'clear status', leaving slight ambiguity. Given the tool's simplicity, this is adequately complete for a 4.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers 100% of the parameter (order_id) with a description ('Order id'). The description does not add format or syntax details beyond what the schema already provides. With schema coverage high, the baseline is 3, and the description adds no extra parameter meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Return real logistics status for an order id'. It specifies a verb (return), resource (logistics status), and scope (for an order id). It is distinct from sibling tools like get_live_quote or get_product, which focus on quotes and products rather than shipment tracking.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies its usage: for retrieving logistics status given an order id. It provides context about fallback behavior ('otherwise returns a clear status and routes to the concierge'), but does not explicitly mention when not to use it or alternatives among siblings. This is clear context without exclusions, warranting a 4.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
get_quote and get_live_quote both return wholesale quotes for product SKUs and quantities, differing only in context (enterprise events vs. general). get_product_catalog and search_products both search the catalog with filters, making the boundary between them unclear. These overlaps can lead to agent misselection.
All tools use snake_case with a verb_noun pattern (get_, generate_, search_, track_). There are minor deviations where similar actions use different verbs (e.g., get_product_catalog vs. search_products, get_quote vs. get_live_quote), but the overall pattern is consistent and predictable.
With 10 tools, the server is well-scoped for its purpose. Each tool covers a distinct aspect of the procurement and event management domain, and the count falls within the ideal 3-15 range, earning its place without feeling bloated.
The server covers product discovery, catalog browsing, quotes, event program generation, and shipment tracking. However, there is no tool for placing an order or managing event records, and the link between quotes and orders is unclear, leaving notable gaps in the end-to-end procurement workflow.